Lab5 + lab5Extra
Nicolas Ferlanti
9/28/2020
Joining Tables
library(tidyverse)## ── Attaching packages ───────────────────────────────────────────── tidyverse 1.3.0 ──## ✓ ggplot2 3.3.2 ✓ purrr 0.3.4
## ✓ tibble 3.0.3 ✓ dplyr 1.0.2
## ✓ tidyr 1.1.2 ✓ stringr 1.4.0
## ✓ readr 1.3.1 ✓ forcats 0.5.0## ── Conflicts ──────────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()Confirmed_State_3_13 <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/03-13-2020.csv")) %>%
rename(Country_Region = "Country/Region", Province_State = "Province/State") %>%
filter (Country_Region == "US") %>%
group_by(Province_State, Country_Region) %>%
summarise(Confirmed = sum(Confirmed)) ## Parsed with column specification:
## cols(
## `Province/State` = col_character(),
## `Country/Region` = col_character(),
## `Last Update` = col_datetime(format = ""),
## Confirmed = col_double(),
## Deaths = col_double(),
## Recovered = col_double(),
## Latitude = col_double(),
## Longitude = col_double()
## )## `summarise()` regrouping output by 'Province_State' (override with `.groups` argument)str(Confirmed_State_3_13)## tibble [53 × 3] (S3: grouped_df/tbl_df/tbl/data.frame)
## $ Province_State: chr [1:53] "Alabama" "Alaska" "Arizona" "Arkansas" ...
## $ Country_Region: chr [1:53] "US" "US" "US" "US" ...
## $ Confirmed : num [1:53] 5 1 9 6 282 49 11 4 46 10 ...
## - attr(*, "groups")= tibble [53 × 2] (S3: tbl_df/tbl/data.frame)
## ..$ Province_State: chr [1:53] "Alabama" "Alaska" "Arizona" "Arkansas" ...
## ..$ .rows : list<int> [1:53]
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## ..- attr(*, ".drop")= logi TRUEConfirmed_State_9_13 <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/09-13-2020.csv")) %>%
filter (Country_Region == "US") %>%
group_by(Province_State, Country_Region) %>%
summarise(Confirmed = sum(Confirmed)) ## Parsed with column specification:
## cols(
## FIPS = col_double(),
## Admin2 = col_character(),
## Province_State = col_character(),
## Country_Region = col_character(),
## Last_Update = col_datetime(format = ""),
## Lat = col_double(),
## Long_ = col_double(),
## Confirmed = col_double(),
## Deaths = col_double(),
## Recovered = col_double(),
## Active = col_double(),
## Combined_Key = col_character(),
## Incidence_Rate = col_double(),
## `Case-Fatality_Ratio` = col_double()
## )## `summarise()` regrouping output by 'Province_State' (override with `.groups` argument)str(Confirmed_State_9_13)## tibble [58 × 3] (S3: grouped_df/tbl_df/tbl/data.frame)
## $ Province_State: chr [1:58] "Alabama" "Alaska" "Arizona" "Arkansas" ...
## $ Country_Region: chr [1:58] "US" "US" "US" "US" ...
## $ Confirmed : num [1:58] 138755 6268 208512 70219 761728 ...
## - attr(*, "groups")= tibble [58 × 2] (S3: tbl_df/tbl/data.frame)
## ..$ Province_State: chr [1:58] "Alabama" "Alaska" "Arizona" "Arkansas" ...
## ..$ .rows : list<int> [1:58]
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## ..- attr(*, ".drop")= logi TRUEConfirmed_State_9_13 <- Confirmed_State_9_13 %>%
filter(Province_State != "Recovered") Confirmed_State_3_13_9_13_joined <- full_join(Confirmed_State_3_13,
Confirmed_State_9_13, by = c("Province_State"))
head(Confirmed_State_3_13_9_13_joined)## # A tibble: 6 x 5
## # Groups: Province_State [6]
## Province_State Country_Region.x Confirmed.x Country_Region.y Confirmed.y
## <chr> <chr> <dbl> <chr> <dbl>
## 1 Alabama US 5 US 138755
## 2 Alaska US 1 US 6268
## 3 Arizona US 9 US 208512
## 4 Arkansas US 6 US 70219
## 5 California US 282 US 761728
## 6 Colorado US 49 US 61293tail(Confirmed_State_3_13_9_13_joined, 5)## # A tibble: 5 x 5
## # Groups: Province_State [5]
## Province_State Country_Region.x Confirmed.x Country_Region.y Confirmed.y
## <chr> <chr> <dbl> <chr> <dbl>
## 1 Wyoming US 1 US 4346
## 2 Guam <NA> NA US 1863
## 3 Northern Mariana Is… <NA> NA US 60
## 4 Puerto Rico <NA> NA US 37380
## 5 Virgin Islands <NA> NA US 1220which(is.na(Confirmed_State_3_13_9_13_joined))## [1] 111 112 113 114 168 169 170 171Confirmed_State_3_13_9_13_joined <- full_join(Confirmed_State_3_13,
Confirmed_State_9_13, by = c("Province_State")) %>%
rename(Confirmed_3_13_2020 = "Confirmed.x", Confirmed_9_13_2020 = "Confirmed.y") %>%
select(-Country_Region.x, -Country_Region.y) %>%
replace_na(list(Confirmed_3_13_2020 = 0))
head(Confirmed_State_3_13_9_13_joined)## # A tibble: 6 x 3
## # Groups: Province_State [6]
## Province_State Confirmed_3_13_2020 Confirmed_9_13_2020
## <chr> <dbl> <dbl>
## 1 Alabama 5 138755
## 2 Alaska 1 6268
## 3 Arizona 9 208512
## 4 Arkansas 6 70219
## 5 California 282 761728
## 6 Colorado 49 61293which(is.na(Confirmed_State_3_13_9_13_joined))## integer(0)Switching between wide and long table formats
Confirmed_State_3_13_9_13_joined_long <- Confirmed_State_3_13_9_13_joined %>%
pivot_longer(-c(Province_State),
names_to = "Date", values_to = "Confirmed")ggplot(Confirmed_State_3_13_9_13_joined_long, aes(x = Confirmed, y = Province_State)) +
geom_point(aes(color = Date)) 
Working with time series data
Data Wrangling
time_series_confirmed <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv")) %>%
rename(Province_State = "Province/State", Country_Region = "Country/Region")## Parsed with column specification:
## cols(
## .default = col_double(),
## `Province/State` = col_character(),
## `Country/Region` = col_character()
## )## See spec(...) for full column specifications. download.file(url="https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv",
destfile = "data/time_series_covid19_confirmed_global.csv")time_series_confirmed <- read_csv("data/time_series_covid19_confirmed_global.csv")%>%
rename(Province_State = "Province/State", Country_Region = "Country/Region")## Parsed with column specification:
## cols(
## .default = col_double(),
## `Province/State` = col_character(),
## `Country/Region` = col_character()
## )## See spec(...) for full column specifications.head(time_series_confirmed)## # A tibble: 6 x 260
## Province_State Country_Region Lat Long `1/22/20` `1/23/20` `1/24/20`
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 <NA> Afghanistan 33.9 67.7 0 0 0
## 2 <NA> Albania 41.2 20.2 0 0 0
## 3 <NA> Algeria 28.0 1.66 0 0 0
## 4 <NA> Andorra 42.5 1.52 0 0 0
## 5 <NA> Angola -11.2 17.9 0 0 0
## 6 <NA> Antigua and B… 17.1 -61.8 0 0 0
## # … with 253 more variables: `1/25/20` <dbl>, `1/26/20` <dbl>, `1/27/20` <dbl>,
## # `1/28/20` <dbl>, `1/29/20` <dbl>, `1/30/20` <dbl>, `1/31/20` <dbl>,
## # `2/1/20` <dbl>, `2/2/20` <dbl>, `2/3/20` <dbl>, `2/4/20` <dbl>,
## # `2/5/20` <dbl>, `2/6/20` <dbl>, `2/7/20` <dbl>, `2/8/20` <dbl>,
## # `2/9/20` <dbl>, `2/10/20` <dbl>, `2/11/20` <dbl>, `2/12/20` <dbl>,
## # `2/13/20` <dbl>, `2/14/20` <dbl>, `2/15/20` <dbl>, `2/16/20` <dbl>,
## # `2/17/20` <dbl>, `2/18/20` <dbl>, `2/19/20` <dbl>, `2/20/20` <dbl>,
## # `2/21/20` <dbl>, `2/22/20` <dbl>, `2/23/20` <dbl>, `2/24/20` <dbl>,
## # `2/25/20` <dbl>, `2/26/20` <dbl>, `2/27/20` <dbl>, `2/28/20` <dbl>,
## # `2/29/20` <dbl>, `3/1/20` <dbl>, `3/2/20` <dbl>, `3/3/20` <dbl>,
## # `3/4/20` <dbl>, `3/5/20` <dbl>, `3/6/20` <dbl>, `3/7/20` <dbl>,
## # `3/8/20` <dbl>, `3/9/20` <dbl>, `3/10/20` <dbl>, `3/11/20` <dbl>,
## # `3/12/20` <dbl>, `3/13/20` <dbl>, `3/14/20` <dbl>, `3/15/20` <dbl>,
## # `3/16/20` <dbl>, `3/17/20` <dbl>, `3/18/20` <dbl>, `3/19/20` <dbl>,
## # `3/20/20` <dbl>, `3/21/20` <dbl>, `3/22/20` <dbl>, `3/23/20` <dbl>,
## # `3/24/20` <dbl>, `3/25/20` <dbl>, `3/26/20` <dbl>, `3/27/20` <dbl>,
## # `3/28/20` <dbl>, `3/29/20` <dbl>, `3/30/20` <dbl>, `3/31/20` <dbl>,
## # `4/1/20` <dbl>, `4/2/20` <dbl>, `4/3/20` <dbl>, `4/4/20` <dbl>,
## # `4/5/20` <dbl>, `4/6/20` <dbl>, `4/7/20` <dbl>, `4/8/20` <dbl>,
## # `4/9/20` <dbl>, `4/10/20` <dbl>, `4/11/20` <dbl>, `4/12/20` <dbl>,
## # `4/13/20` <dbl>, `4/14/20` <dbl>, `4/15/20` <dbl>, `4/16/20` <dbl>,
## # `4/17/20` <dbl>, `4/18/20` <dbl>, `4/19/20` <dbl>, `4/20/20` <dbl>,
## # `4/21/20` <dbl>, `4/22/20` <dbl>, `4/23/20` <dbl>, `4/24/20` <dbl>,
## # `4/25/20` <dbl>, `4/26/20` <dbl>, `4/27/20` <dbl>, `4/28/20` <dbl>,
## # `4/29/20` <dbl>, `4/30/20` <dbl>, `5/1/20` <dbl>, `5/2/20` <dbl>,
## # `5/3/20` <dbl>, …time_series_confirmed_long <- time_series_confirmed %>%
pivot_longer(-c(Province_State, Country_Region, Lat, Long),
names_to = "Date", values_to = "Confirmed")
head(time_series_confirmed_long)## # A tibble: 6 x 6
## Province_State Country_Region Lat Long Date Confirmed
## <chr> <chr> <dbl> <dbl> <chr> <dbl>
## 1 <NA> Afghanistan 33.9 67.7 1/22/20 0
## 2 <NA> Afghanistan 33.9 67.7 1/23/20 0
## 3 <NA> Afghanistan 33.9 67.7 1/24/20 0
## 4 <NA> Afghanistan 33.9 67.7 1/25/20 0
## 5 <NA> Afghanistan 33.9 67.7 1/26/20 0
## 6 <NA> Afghanistan 33.9 67.7 1/27/20 0 download.file(url="https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv",
destfile = "data/time_series_covid19_deaths_global.csv")time_series_deaths <- read_csv("data/time_series_covid19_deaths_global.csv")%>%
rename(Province_State = "Province/State", Country_Region = "Country/Region")## Parsed with column specification:
## cols(
## .default = col_double(),
## `Province/State` = col_character(),
## `Country/Region` = col_character()
## )## See spec(...) for full column specifications.time_series_deaths_long <- time_series_deaths %>%
pivot_longer(-c(Province_State, Country_Region, Lat, Long),
names_to = "Date", values_to = "Deaths")
head(time_series_deaths_long)## # A tibble: 6 x 6
## Province_State Country_Region Lat Long Date Deaths
## <chr> <chr> <dbl> <dbl> <chr> <dbl>
## 1 <NA> Afghanistan 33.9 67.7 1/22/20 0
## 2 <NA> Afghanistan 33.9 67.7 1/23/20 0
## 3 <NA> Afghanistan 33.9 67.7 1/24/20 0
## 4 <NA> Afghanistan 33.9 67.7 1/25/20 0
## 5 <NA> Afghanistan 33.9 67.7 1/26/20 0
## 6 <NA> Afghanistan 33.9 67.7 1/27/20 0Joining the times series tables
time_series_confirmed_long <- time_series_confirmed_long %>%
unite(Key, Province_State, Country_Region, Date, sep = ".", remove = FALSE)
head(time_series_confirmed_long)## # A tibble: 6 x 7
## Key Province_State Country_Region Lat Long Date Confirmed
## <chr> <chr> <chr> <dbl> <dbl> <chr> <dbl>
## 1 NA.Afghanistan.1/2… <NA> Afghanistan 33.9 67.7 1/22/… 0
## 2 NA.Afghanistan.1/2… <NA> Afghanistan 33.9 67.7 1/23/… 0
## 3 NA.Afghanistan.1/2… <NA> Afghanistan 33.9 67.7 1/24/… 0
## 4 NA.Afghanistan.1/2… <NA> Afghanistan 33.9 67.7 1/25/… 0
## 5 NA.Afghanistan.1/2… <NA> Afghanistan 33.9 67.7 1/26/… 0
## 6 NA.Afghanistan.1/2… <NA> Afghanistan 33.9 67.7 1/27/… 0time_series_deaths_long <- time_series_deaths_long %>%
unite(Key, Province_State, Country_Region, Date, sep = ".") %>%
select(Key, Deaths)time_series_long_joined <- full_join(time_series_confirmed_long,
time_series_deaths_long, by = c("Key")) %>%
select(-Key)
head(time_series_long_joined)## # A tibble: 6 x 7
## Province_State Country_Region Lat Long Date Confirmed Deaths
## <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl>
## 1 <NA> Afghanistan 33.9 67.7 1/22/20 0 0
## 2 <NA> Afghanistan 33.9 67.7 1/23/20 0 0
## 3 <NA> Afghanistan 33.9 67.7 1/24/20 0 0
## 4 <NA> Afghanistan 33.9 67.7 1/25/20 0 0
## 5 <NA> Afghanistan 33.9 67.7 1/26/20 0 0
## 6 <NA> Afghanistan 33.9 67.7 1/27/20 0 0which(is.na(time_series_long_joined$Confirmed))## integer(0)which(is.na(time_series_long_joined$Deaths))## integer(0)library(lubridate)##
## Attaching package: 'lubridate'## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, uniontime_series_long_joined$Date <- mdy(time_series_long_joined$Date)time_series_long_joined_counts <- time_series_long_joined %>%
pivot_longer(-c(Province_State, Country_Region, Lat, Long, Date),
names_to = "Report_Type", values_to = "Counts")
head(time_series_long_joined_counts)## # A tibble: 6 x 7
## Province_State Country_Region Lat Long Date Report_Type Counts
## <chr> <chr> <dbl> <dbl> <date> <chr> <dbl>
## 1 <NA> Afghanistan 33.9 67.7 2020-01-22 Confirmed 0
## 2 <NA> Afghanistan 33.9 67.7 2020-01-22 Deaths 0
## 3 <NA> Afghanistan 33.9 67.7 2020-01-23 Confirmed 0
## 4 <NA> Afghanistan 33.9 67.7 2020-01-23 Deaths 0
## 5 <NA> Afghanistan 33.9 67.7 2020-01-24 Confirmed 0
## 6 <NA> Afghanistan 33.9 67.7 2020-01-24 Deaths 0Making graphs from time series data
time_series_long_joined %>%
group_by(Country_Region,Date) %>%
summarise_at(c("Confirmed", "Deaths"), sum) %>%
filter (Country_Region == "US") %>%
ggplot(aes(x = Date, y = Deaths)) +
geom_point() +
geom_line() +
ggtitle("US COVID-19 Deaths")
time_series_long_joined %>%
group_by(Country_Region,Date) %>%
summarise_at(c("Confirmed", "Deaths"), sum) %>%
filter (Country_Region %in% c("China","Japan", "Korea, South",
"Italy","Spain", "US")) %>%
ggplot(aes(x = Date, y = Deaths)) +
geom_point() +
geom_line() +
ggtitle("COVID-19 Deaths") +
facet_wrap(~Country_Region, ncol=2, scales="free_y")
time_series_long_joined %>%
group_by(Country_Region,Date) %>%
summarise_at(c("Confirmed", "Deaths"), sum) %>%
filter (Country_Region %in% c("China","France","Italy",
"Korea, South", "US")) %>%
ggplot(aes(x = Date, y = Deaths, color = Country_Region)) +
geom_point() +
geom_line() +
ggtitle("COVID-19 Deaths")
time_series_long_joined_counts %>%
group_by(Country_Region, Report_Type, Date) %>%
summarise(Counts = sum(Counts)) %>%
filter (Country_Region == "US") %>%
ggplot(aes(x = Date, y = log2(Counts), fill = Report_Type, color = Report_Type)) +
geom_point() +
geom_line() +
ggtitle("US COVID-19 Cases")## `summarise()` regrouping output by 'Country_Region', 'Report_Type' (override with `.groups` argument)
Exercises
Exercise 2 + 3
Confirmed_State_6_13 <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/06-13-2020.csv")) %>%
filter (Country_Region == "US") %>%
group_by(Province_State, Country_Region) %>%
summarise(Confirmed = sum(Confirmed)) ## Parsed with column specification:
## cols(
## FIPS = col_double(),
## Admin2 = col_character(),
## Province_State = col_character(),
## Country_Region = col_character(),
## Last_Update = col_datetime(format = ""),
## Lat = col_double(),
## Long_ = col_double(),
## Confirmed = col_double(),
## Deaths = col_double(),
## Recovered = col_double(),
## Active = col_double(),
## Combined_Key = col_character(),
## Incidence_Rate = col_double(),
## `Case-Fatality_Ratio` = col_double()
## )## `summarise()` regrouping output by 'Province_State' (override with `.groups` argument)str(Confirmed_State_6_13)## tibble [58 × 3] (S3: grouped_df/tbl_df/tbl/data.frame)
## $ Province_State: chr [1:58] "Alabama" "Alaska" "Arizona" "Arkansas" ...
## $ Country_Region: chr [1:58] "US" "US" "US" "US" ...
## $ Confirmed : num [1:58] 24601 653 34660 12095 150018 ...
## - attr(*, "groups")= tibble [58 × 2] (S3: tbl_df/tbl/data.frame)
## ..$ Province_State: chr [1:58] "Alabama" "Alaska" "Arizona" "Arkansas" ...
## ..$ .rows : list<int> [1:58]
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## ..- attr(*, ".drop")= logi TRUEsetdiff(Confirmed_State_6_13$Province_State, Confirmed_State_9_13$Province_State)## [1] "Recovered"Confirmed_State_6_13 <- Confirmed_State_6_13 %>%
filter(Province_State != "Recovered") Confirmed_State_6_13_9_13_joined <- full_join(Confirmed_State_6_13,
Confirmed_State_9_13, by = c("Province_State"))
head(Confirmed_State_6_13_9_13_joined)## # A tibble: 6 x 5
## # Groups: Province_State [6]
## Province_State Country_Region.x Confirmed.x Country_Region.y Confirmed.y
## <chr> <chr> <dbl> <chr> <dbl>
## 1 Alabama US 24601 US 138755
## 2 Alaska US 653 US 6268
## 3 Arizona US 34660 US 208512
## 4 Arkansas US 12095 US 70219
## 5 California US 150018 US 761728
## 6 Colorado US 29002 US 61293tail(Confirmed_State_6_13_9_13_joined, 5)## # A tibble: 5 x 5
## # Groups: Province_State [5]
## Province_State Country_Region.x Confirmed.x Country_Region.y Confirmed.y
## <chr> <chr> <dbl> <chr> <dbl>
## 1 Virginia US 53869 US 133742
## 2 Washington US 25538 US 79826
## 3 West Virginia US 2274 US 12705
## 4 Wisconsin US 22518 US 89185
## 5 Wyoming US 1050 US 4346which(is.na(Confirmed_State_6_13_9_13_joined))## integer(0)Confirmed_State_6_13_9_13_joined <- full_join(Confirmed_State_6_13,
Confirmed_State_9_13, by = c("Province_State")) %>%
rename(Confirmed_6_13_2020 = "Confirmed.x", Confirmed_9_13_2020 = "Confirmed.y") %>%
select(-Country_Region.x, -Country_Region.y) %>%
replace_na(list(Confirmed_6_13_2020 = 0))
head(Confirmed_State_6_13_9_13_joined)## # A tibble: 6 x 3
## # Groups: Province_State [6]
## Province_State Confirmed_6_13_2020 Confirmed_9_13_2020
## <chr> <dbl> <dbl>
## 1 Alabama 24601 138755
## 2 Alaska 653 6268
## 3 Arizona 34660 208512
## 4 Arkansas 12095 70219
## 5 California 150018 761728
## 6 Colorado 29002 61293which(is.na(Confirmed_State_6_13_9_13_joined))## integer(0)Confirmed_State_6_13_9_13_joined_long <- Confirmed_State_6_13_9_13_joined %>%
pivot_longer(-c(Province_State),
names_to = "Date", values_to = "Confirmed")ggplot(data = Confirmed_State_6_13_9_13_joined_long, aes(x = Province_State, y = Confirmed, fill = Date)) +
geom_bar(stat="identity", position = position_dodge()) +
scale_y_continuous(labels = scales::comma) +
labs(x = "Province/State", y = "Confirmed Cases", title = "Confirmed Cases by Province/State for 6/13/2020 and 9/13/2020") +
theme_bw() +
theme(axis.text.x = element_text(colour = "grey20", size = 10, angle = 90, hjust = 0.5, vjust = 0.5),
axis.text.y = element_text(colour = "grey20", size = 12),
strip.text = element_text(face = "italic"),
text = element_text(size = 12))
Same graph as above but stacked barplot
ggplot(data = Confirmed_State_6_13_9_13_joined_long, aes(x = Province_State, y = Confirmed, fill = Date)) +
geom_bar(stat="identity") +
labs(x = "Province/State", y = "Confirmed", title = "Confirmed Cases by Province/State for 6/13/2020 and 9/13/2020") +
theme_bw() +
theme(axis.text.x = element_text(colour = "grey20", size = 10, angle = 90, hjust = 0.5, vjust = 0.5),
axis.text.y = element_text(colour = "grey20", size = 12),
strip.text = element_text(face = "italic"),
text = element_text(size = 12))
Exercise 4
time_series_long_joined %>%
group_by(Date) %>%
summarise_at(c("Deaths"), sum) %>%
ggplot(aes(x = Date, y = Deaths)) +
geom_line(size = 1) +
ggtitle("COVID-19 Deaths Worldwide")
Exercise 5
time_series_long_joined_new <- time_series_long_joined %>%
mutate(deaths_confirmed = Deaths / Confirmed) %>%
select(Country_Region, Date, Deaths, Confirmed, deaths_confirmed)
view(time_series_long_joined_new)Exercise 6
time_series_long_joined_new %>%
group_by(Country_Region,Date) %>%
summarise_at(c("deaths_confirmed"), sum) %>%
filter (Country_Region == "US") %>%
ggplot(aes(x = Date, y = deaths_confirmed)) +
geom_line() +
labs(title = "US COVID-19 Deaths/Confirmed Cases", y = "Deaths/Confirmed Cases")
Exercise 7
top10 <- time_series_long_joined %>%
group_by(Country_Region, Date) %>%
summarise(Deaths = sum(Deaths)) %>%
arrange(desc(Deaths)) %>%
slice(1) %>%
arrange(desc(Deaths)) %>%
head(10)## `summarise()` regrouping output by 'Country_Region' (override with `.groups` argument)view(top10)time_series_long_joined %>%
group_by(Country_Region,Date) %>%
summarise(Deaths = sum(Deaths)) %>%
filter (Country_Region %in% c("US","Brazil","Italy",
"India, Mexico", "United Kingdom", "Peru", "France", "Spain", "Iran")) %>%
ggplot(aes(x = Date, y = Deaths, color = Country_Region)) +
geom_line() +
ggtitle("Top 10 Countries with Most COVID-19 Deaths")## `summarise()` regrouping output by 'Country_Region' (override with `.groups` argument)
Exercise 8
time_series_long_joined %>%
group_by(Country_Region,Date) %>%
summarise(Deaths = sum(Deaths)) %>%
filter (Country_Region %in% c("US","Brazil","Italy",
"India, Mexico", "United Kingdom", "Peru", "France", "Spain", "Iran")) %>%
ggplot(aes(x = Date, y = Deaths, color = Country_Region)) +
geom_line() +
ggtitle("Top 10 Countries with Most COVID-19 Deaths") +
facet_wrap(~Country_Region, ncol=2, scales="free_y")## `summarise()` regrouping output by 'Country_Region' (override with `.groups` argument)
Exercise 9
time_series_confirmed_US <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_US.csv")) %>%
rename(Province_State = "Province_State", Country_Region = "Country_Region")## Parsed with column specification:
## cols(
## .default = col_double(),
## iso2 = col_character(),
## iso3 = col_character(),
## Admin2 = col_character(),
## Province_State = col_character(),
## Country_Region = col_character(),
## Combined_Key = col_character()
## )## See spec(...) for full column specifications.timeUS <- time_series_confirmed_US %>%
group_by(Province_State) %>%
select(-c(UID, iso2, iso3, code3, FIPS, Admin2, Lat, Long_, Combined_Key, Country_Region)) %>%
pivot_longer(-c(Province_State), names_to = "Date", values_to = "Confirmed") %>%
group_by(Province_State, Date) %>%
summarise(Confirmed = sum(Confirmed))## `summarise()` regrouping output by 'Province_State' (override with `.groups` argument)view(timeUS)timeUS %>%
ggplot(aes(x = Date, y = Confirmed)) +
geom_point(size = 0.1) +
facet_wrap(vars(Province_State), ncol = 5) +
labs(title = "Confirmed Cases per US State/Province Over Time", x = "Date", y = "Confirmed Cases")
Exercise 10
time_series_long_joined %>%
group_by(Country_Region,Date) %>%
summarise(Deaths = sum(Deaths)) %>%
filter (Country_Region %in% c("US","Brazil","Italy",
"India, Mexico", "United Kingdom", "Peru", "France", "Spain", "Iran")) %>%
ggplot(aes(x = Date, y = Deaths, color = Country_Region)) +
geom_line() +
ggtitle("Top 10 Countries with Most COVID-19 Deaths") +
facet_wrap(~Country_Region, ncol=2, scales="free_y") +
theme_dark()## `summarise()` regrouping output by 'Country_Region' (override with `.groups` argument)
Lab 5 Extra
Graphic Output
# Plot graph to a pdf outputfile
pdf("images/time_series_example_plot.pdf", width=6, height=3)
time_series_long_joined %>%
group_by(Country_Region,Date) %>%
summarise_at(c("Confirmed", "Deaths"), sum) %>%
filter (Country_Region == "US") %>%
ggplot(aes(x = Date, y = Deaths)) +
geom_point() +
geom_line() +
ggtitle("US COVID-19 Deaths")
dev.off()## png
## 2# Plot graph to a png outputfile
ppi <- 300
png("images/time_series_example_plot.png", width=6*ppi, height=6*ppi, res=ppi)
time_series_long_joined %>%
group_by(Country_Region,Date) %>%
summarise_at(c("Confirmed", "Deaths"), sum) %>%
filter (Country_Region == "US") %>%
ggplot(aes(x = Date, y = Deaths)) +
geom_point() +
geom_line() +
ggtitle("US COVID-19 Deaths")
dev.off()## png
## 2RMarkdown Loading Images
# This is the RMarkdown style for inserting images
# Your image must be in your working directory
# This command is put OUTSIDE the r code chunk
US COVID-19 Deaths
# This is an alternative way using html.
# Remember that it must be in your working directory or you will need to specify the full path.
# The html is put OUTSIDE the r code chunk.
Interactive Graphs
# Version 2
library(plotly)##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layoutggplotly(
time_series_long_joined %>%
group_by(Country_Region,Date) %>%
summarise_at(c("Confirmed", "Deaths"), sum) %>%
filter (Country_Region == "US") %>%
ggplot(aes(x = Date, y = Deaths)) +
geom_point() +
geom_line() +
ggtitle("US COVID-19 Deaths")
)library(plotly)
# Subset the time series data to include US deaths
US_deaths <- time_series_long_joined %>%
group_by(Country_Region,Date) %>%
summarise_at(c("Confirmed", "Deaths"), sum) %>%
filter (Country_Region == "US")
# Collect the layers for agraph of the US time series data for covid deaths
p <- ggplot(data = US_deaths, aes(x = Date, y = Deaths)) +
geom_point() +
geom_line() +
ggtitle("US COVID-19 Deaths")
# Plot the graph using ggplotly
ggplotly(p)Animated Graphs with gganimate
library(gganimate)
library(transformr)
theme_set(theme_bw())data_time <- time_series_long_joined %>%
group_by(Country_Region,Date) %>%
summarise_at(c("Confirmed", "Deaths"), sum) %>%
filter (Country_Region %in% c("China","Korea, South","Japan","Italy","US"))
p <- ggplot(data_time, aes(x = Date, y = Confirmed, color = Country_Region)) +
geom_point() +
geom_line() +
ggtitle("Confirmed COVID-19 Cases") +
geom_point(aes(group = seq_along(Date))) +
transition_reveal(Date)
animate(p,renderer = gifski_renderer(), end_pause = 15)
data_time <- time_series_long_joined %>%
group_by(Country_Region,Date) %>%
summarise_at(c("Confirmed", "Deaths"), sum) %>%
filter (Country_Region %in% c("China","Korea, South","Japan","Italy","US"))
p <- ggplot(data_time, aes(x = Date, y = Confirmed, color = Country_Region)) +
geom_point() +
geom_line() +
ggtitle("Confirmed COVID-19 Cases") +
geom_point(aes(group = seq_along(Date))) +
transition_reveal(Date)
anim_save("deaths_5_countries.gif", p,renderer = gifski_renderer(), end_pause = 15)